基于Log-Gabor滤波特征的黎曼流形图像集分类算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201704010

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Abstract The perception theory of biological neurology coincides with Riemannian manifold, and Log-Gabor filter is more suitable for nonlinear human eye logarithmic characteristic than other filters.Therefore,the combination of Log-Gabor wavelet and Riemannian manifold accords with the process of human visual perception. Grounded on covariance discriminative learning(CDL), the Riemannian manifold image set classification algorithm based on Log-Gabor Wavelet features is presented.Each image is processed by Log-Gabor filter to get its multi-scale and multi-direction features. The two-directional two-dimensional principal component analysis is adopted to reduce the dimension of covariance matrix and then the covariance discriminative learning algorithm is applied for classification.The experimental results of the proposed algorithm on several standard datasets show the superiority of the algorithm in accuracy over state-of-the-art algorithms.

Key words： Covariance Discriminative Learning(CDL) Riemannian Manifold Kernel Discriminant Analysis(KDA) Two-Directional Two-Dimensional Principal Component Analysis

Received: 22 August 2016

ZTFLH:

TP 391.4

Fund:Supported by National Natural Science Foundation of China(No.61672265,61373055), Industrialization Project of Jiangsu Educational Department(No.JH10-28), Production and Research Innovation Project of Jiangsu Province(No.BY2012059)

About author:: (WANG Rui, born in 1992, master student. His research interests include Riemannian manifold and feature extraction.)
(WU Xiaojun(Corresponding author), born in 1967, Ph.D., professor. His research interests include artificial intelligence, pattern recognition and computer vision.)

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WANG Rui,WU Xiaojun. Riemannian Manifold Image Set Classification Algorithm Based on Log-Gabor Wavelet Features[J]. , 2017, 30(4): 377-384.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201704010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I4/377

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